Reducing coal quality attributes variability using properly designed blending piles helped by geostatistical simulation

Beretta, Filipe Schmitz; Costa, João Felipe Coimbra Leite; Koppe, Jair Carlos

doi:10.1016/j.coal.2010.08.007

Cited by 17 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…В зарубежной литературе эти вопросы получи-ли освещение в работах (Gy, 1981;Petersen, 2004;Robinson, 2004;Kumral, 2005;Beretta, Costa, & Koppe, 2010;Muller, 2010;Huang & Chen, 2011) и др. В рус-скоязычной научной литературе -представлены в работах (Bastan, Azbel', & Klyuchkin, 1979;Galiev, 1997;Mosinets & Rubtsov, 2001;Vashlaev & Selivanov, 2004;Vasil'yev, 2007;Tsekhovoy, Zhusupov, & Statsenko, 2009;Shevelev, 2014) и др.…”

Section: анализ этапов внутрикарьерной стабилизации качества полезногunclassified

Developing information systems of operation schedules to stabilize the grade of a mineral

Zarubin¹,

Statsenko²,

Zarubina³

et al. 2017

Min. miner. depos,

View full text Add to dashboard Cite

Section: анализ этапов внутрикарьерной стабилизации качества полезногunclassified

Developing information systems of operation schedules to stabilize the grade of a mineral

Zarubin¹,

Statsenko²,

Zarubina³

et al. 2017

Min. miner. depos,

View full text Add to dashboard Cite

“…An appropriate geological model with spatial distributions of the parameters can improve the reliability of CBM production prediction. Geostatistics was used to estimate the distributions of coal thickness (Jakeman, 1980;Mastalerz and Kenneth, 1994;Zhou et al, 2012), coal quality (Cairncross and Cadle, 1988;Hagelskamp et al, 1988;Liu et al, 2005;Heriawan and Koike, 2008;Beretta et al, 2010;Hindistan et al, 2010) and coal properties. These distributions can be used to assess the uncertainty in CBM production.…”

Section: Introductionmentioning

confidence: 99%

“…The adsorption data are sourced from experiments. The distribution of these parameters can then be predicted using a geostatistical method such as SGS and ordinary kriging (Beretta et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Effects of Variogram Characteristics of Coal Permeability on CBM Production: A Case Study in Southeast Qinshui Basin, China

Zhou

Yao

Jian-zhong

2014

Energy Exploration & Exploitation

View full text Add to dashboard Cite

The coalbed methane (CBM) resources of China are located mainly in 9 basins, Ordos, Qinshui, Jungar, Diandongqianxi, Erlian, Tuha, Tarim, Tianshan and Hailaer. Qinshui Basin, one of the richest CBM basins in China, has boosted its annual CBM production to 3×10 9 m 3 (106 Bcf). The coal seams in Qinshui Basin are significant with high gas content but strong heterogeneous permeabilities ranging from 0.1 to 10 mD. This paper investigates the effects of spatial distribution characteristics of coal permeability on CBM production. The study area is the South Shizhuang CBM district, Southeast of Qinshui Basin. The distributions of porosity, ash content, coal density and gas content of the coal seam are generated using sequential Gaussian simulation (SGS) with only one realisation because this paper only justifies the effects of coal permeability on CBM production. The permeabilities of 17 wells are determined by matching these wells' water and gas production with bottom-hole pressure as constraint. Then, the distributions of coal permeabilities are generated using SGS with a commercial simulator. The history matched permeabilities range from 1.5 to 12 mD with average of 2.9 mD of the 17 wells. Eight variogram models are used to build the distributions of permeability. The cumulative gas productions of two different well-spacing cases, 300 m and 2000 m, are compared. There are 20 realisations of permeability for each of the eight models. The results show that historical matching can be used to obtain the porosity multipliers and the permeabilities in wells. The major direction of variogram has less effect on the uncertainty of field CBM production than variogram range. The effects of variogram range on the uncertainty of CBM production are positive for the case with short well spacing and vice versa for the case with long well spacing.

show abstract

“…Geostatistical methods, which are optimal when data of the modeled parameters are stationary (mean and variance or covariance do not vary significantly in space), are normally used to generate the distributions of coal properties and coal gas content (Jakeman, 1980;Mastalerz and Kenneth, 1994;Cairncross and Cadle, 1988;Hagelskamp et al, 1988;Liu et al, 2005;Heriawan and Koike, 2008b;Beretta et al, 2010;Hindistan et al, 2010;Heriawan and Koike, 2008a;Karacan et al, 2012;Karacan et al, 2014;Karacan and Olea, 2015). SGS and ordinary kriging (OK) methods (Bohling, 2005) were used to predict the distribution of coal thickness and coal quality (Beretta et al, 2010).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…SGS and ordinary kriging (OK) methods (Bohling, 2005) were used to predict the distribution of coal thickness and coal quality (Beretta et al, 2010). Geostatistical modeling also was used to analyze the uncertainty of total CBM resources (Zhou et al 2012), gas retained amount in coal gas emission zone (Karacan et al, 2012), time-lapse gas-in-place after degasification using vertical wells (Karacan, 2013;Karacan and Olea, 2013) and effect of variogram characteristics of permeability on CBM production (Zhou et al, 2014).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Impact of geological modeling processes on spatial coalbed methane resource estimation

Zhou

Yao

Tyson

2015

International Journal of Coal Geology

View full text Add to dashboard Cite

Spatial coalbed methane (CBM) resources estimation is based on spatial distributions of coal, coal adsorbed gas content and coal density. However, the spatial distribution of gas content can be generated via two different geological modeling processes: (1) The gas content distribution is generated by geological modeling based on the interpreted gas content at boreholes; (2) Distributions of gas content related logs or coal properties are generated firstly, then the gas content distribution is calculated based on the spatial distributions of logs or coal properties by the relationship between the gas content and logs or coal properties. This paper presents a study to compare the impact of these two processes on CBM resources estimation for coal seam No.3 (CS-3) in southeast Qinshui Basin, China. Well logs from 22 wells, laboratory data from five wells and well tops from 131 wells for CS-3 are used in log interpretation and geological modeling. The simple kriging (SK) is used to build the structural model and the coal distribution. * Corresponding author:Tel.: +61 7 3365 1180, fax: +61 7 3365 1277 E-mail address: f.zhou1@uq.edu.au (F. Zhou) A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT2 Weighted and unweighted omni-directional variograms for structural residual and coal thickness are calculated using an in-house program. Logs of gamma-ray (GR) and density (DEN or RHOB) are distributed in 3D by using sequential Gaussian simulation (SGS) with SK algorithm. Artificial neural network (ANN) is used to build the relationship of the measured raw gas content (RGC; gas content in raw coal basis) with the logs of GR, DEN and measured depth (MD). Then the RGC is distributed in 3D by the two geological modeling processes. CBM resources are calculated in 3D based on the cells' volume, coal density and RGC. Results show that RGC increases with increase in burial depth. Total CBM resources for the study area calculated by these two processes are similar for CS-3 but the distribution probability of high gas content is highly different which is important for locating wells.

show abstract

Reducing coal quality attributes variability using properly designed blending piles helped by geostatistical simulation

Cited by 17 publications

References 4 publications

Developing information systems of operation schedules to stabilize the grade of a mineral

Developing information systems of operation schedules to stabilize the grade of a mineral

Effects of Variogram Characteristics of Coal Permeability on CBM Production: A Case Study in Southeast Qinshui Basin, China

Impact of geological modeling processes on spatial coalbed methane resource estimation

Contact Info

Product

Resources

About